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Using Contextual Information to Understand
Searching and Browsing BehaviorJulia Kiseleva
Eindhoven University of Technology
Eindhoven, The Netherlands, June 2016
Using Contextual Information to Understand
Searching and Browsing Behavior
Searching Behavior
Want to go to CIKM
conference
QUERY SERP
Browsing Behavior
User Preferences
Using Contextual Information to Understand
Searching and Browsing Behavior
Contextual InformationExplicit Context Implicit Context
Contextual InformationExplicit Context Implicit Context
Contextual InformationExplicit Context Implicit Context
Contextual Situations
(Android Tablet, Weekend)
Photo credit: Delwin Steven Campbell via Visualhunt.com / CC BY
Using Contextual Information to Understand
Searching and Browsing Behavior
Our Main Research GoalHow to
usecontextual information
in order tounderstand
users’ searching and browsing
behavior on the web?
Improve Online User Experience
Applied StudiesBrowsing Behavior
Destination Finder
Chapter 3 ‘Contextual Profiles’. L. Bernardi et al. The continuous cold start problem in e-commerce recommender systems. CBRecSys. 2015J. Kiseleva et al. Where to go on your next trip? optimizing travel destinations based on user preferences. SIGIR. 2015
Destination Finder
Chapter 3 ‘Contextual Profiles’. L. Bernardi et al. The continuous cold start problem in e-commerce recommender systems. CBRecSys. 2015J. Kiseleva et al. Where to go on your next trip? optimizing travel destinations based on user preferences. SIGIR. 2015
Destination Finder
Chapter 3 ‘Contextual Profiles’. L. Bernardi et al. The continuous cold start problem in e-commerce recommender systems. CBRecSys. 2015J. Kiseleva et al. Where to go on your next trip? optimizing travel destinations based on user preferences. SIGIR. 2015
Destination Finder
Optimized Ranking of DestinationsUsing Contextual Situations
Increased User Engagement (Click Trough Rate +3.7%)
Chapter 3 ‘Contextual Profiles’. L. Bernardi et al. The continuous cold start problem in e-commerce recommender systems. CBRecSys. 2015J. Kiseleva et al. Where to go on your next trip? optimizing travel destinations based on user preferences. SIGIR. 2015
Applied StudiesBrowsing Behavior
Applied StudiesBrowsing Behavior Searching Behavior
&
Changes in User Satisfaction
Want to go to CIKM
conference
QUERY SERP
Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
Changes in User Satisfaction
QUERY SERP,Dynamic over Time
Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
Changes in User Satisfaction
Time
Sati
sfac
tion
Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
QUERY , SERP
Changes in User Satisfaction
Time
#
Refo
rmul
atio
ns~
Sati
sfac
tion
Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
2013Oct NovSepAugJul
QUERY , SERP
Changes in User SatisfactionBefore November 2013
After November 2013
Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
QUERY= ‘flawless’
Changes in User SatisfactionBefore November 2013
After November 2013
Chapter 7 ‘Query Reformulations’ and Chapter 8 ‘Failed SERPs’J. Kiseleva et al. Modelling and detecting changes in user satisfaction. CIKM. 2014J. Kiseleva et al. Behavioral dynamics from the SERP’s perspective: What are failed SERPs and how to fix them? CIKM. 2015
QUERY= ‘flawless’
Applied StudiesBrowsing Behavior Searching Behavior
&
Cortana:“What can I
help you do now?”
Q1: how is the weather in ChicagoQ2: how is it this weekendQ3: find me hotelsQ4: which one of these is the cheapestQ5: which one of these has at least 4 starsQ6: find me directions from the Chicago airport to number one
User’s dialogue with
Cortana:Task is
“Finding a hotel in
Chicago”
Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
Q1: find me a pharmacy nearbyQ2: which of these is highly ratedQ3: show more information about number 2Q4: how long will it take me to get thereQ5: Thanks
User’s dialogue with
Cortana:Task is
“Finding a pharmacy”
Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
Cortana: “Here are
ten restaurant
s near you”
Cortana:“Here are ten restaurants
near you that have good reviews”
Cortana:“Getting you direction to the Mayuri
Indian Cuisine”
User:“show restaur
ants near me”
User:“show
the best ones”
User:“show
directions to the second one”
Cortana: “Here are
ten restaurant
s near you”
Cortana:“Here are ten restaurants
near you that have good reviews”
Cortana:“Getting you direction to the Mayuri
Indian Cuisine”
User:“show restaur
ants near me”
User:“show
the best ones”
User:“show
directions to the second one”
No Clicks ??
?
Cortana: “Here are
ten restaurant
s near you”
Cortana:“Here are ten restaurants
near you that have good reviews”
Cortana:“Getting you direction to the Mayuri
Indian Cuisine”
User:“show restaur
ants near me”
User:“show
the best ones”
User:“show
directions to the second one”
SAT?
SAT?
SAT?
Overall SAT? ? SAT
?SAT
?SAT
?
Acoustic Similarity
Phonetic Similarity
Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
Tracking User Interaction
Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
3 seconds
6 seconds33% of
ViewPort 66% of
ViewPort
View
Port
H
eigh
t
2 seconds20% of ViewPor
t
1s 4s 0.4s 5.4s+ + =
Tracking User Interaction
Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
Quality of Interaction Model
Method Accuracy (%) Average F1 (%)Baseline 70.62 61.38
Interaction Model 80.81*(14.43)
79.08*(28.83)
* Statistically significant improvement (p < 0,05 )
Chapter 4 ‘Intelligent Assistants’ and Chapter 5 ‘Search Dialogues’J. Kiseleva et al. Understanding user satisfaction with intelligent assistants. CHIIR 2016J. Kiseleva et al. Predicting user satisfaction with intelligent assistants. SIGIR 2016
• Contextual information should be taken into account to understand web and mobile users’ behavior
• Analyzing behavioral signals over time is needed to detect changes in user satisfaction with web search
• Touch signals are crucial for inferring user satisfaction with intelligent assistants on mobile devices
Conclusion
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